论文标题
Facebook数据中心的深度学习培训：扩展和扩展系统的设计
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems
论文作者
论文摘要
大规模训练对于确保机器学习模型的高性能和准确性很重要。在Facebook，我们使用许多不同的模型，包括计算机视觉，视频和语言模型。但是，在本文中，我们专注于深度学习推荐模型（DLRMS），这些模型负责超过50％的数据中心培训需求。推荐模型在训练中带来了独特的挑战，因为它们不仅锻炼了记忆容量，而且内存和网络带宽。随着模型大小和复杂性的增加，有效扩展训练成为挑战。为了解决它，我们设计锡安 - Facebook的下一代大型内存培训平台，由CPU和加速器组成。此外，我们讨论了未来扩展培训系统的设计要求。
Large-scale training is important to ensure high performance and accuracy of machine-learning models. At Facebook we use many different models, including computer vision, video and language models. However, in this paper we focus on the deep learning recommendation models (DLRMs), which are responsible for more than 50% of the training demand in our data centers. Recommendation models present unique challenges in training because they exercise not only compute but also memory capacity as well as memory and network bandwidth. As model size and complexity increase, efficiently scaling training becomes a challenge. To address it we design Zion - Facebook's next-generation large-memory training platform that consists of both CPUs and accelerators. Also, we discuss the design requirements of future scale-out training systems.
